多基因预测治疗疗效与因果转移学习因果转移学习
Jiacheng Miao1,2, Jin Mu3, Xiaoyu Yang3
1Department of Genetics, Stanford University.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
识别导致异质治疗效应 (HTE) 的遗传因素是精准医学的关键. M-Learner框架使用遗传变异来发现这些差异,改进个性化治疗策略.
科学领域:
- 遗传学 是一个遗传学.
- 药理学 药理学是指药理学的学科.
- 生物统计学 生物统计学
背景情况:
- 治疗干预表明患者的反应是可变的 (异质治疗效应,HTE).
- 精准医学旨在利用患者特征个性化治疗.
- 在临床试验中,由于样本规模小,数据缺失和低功率的统计方法,检测HTE很困难.
研究的目的:
- 引入一个新的统计框架,M-Learner,以识别基因驱动的HTE.
- 利用遗传变异和因果转移学习来推断HTE.
- 能够提供个性化的治疗建议.
主要方法:
- 开发了M-Learner统计框架,用于识别基因驱动的HTE.
- 利用跨生物通路的遗传变异影响药物反应.
- 员工因果转移学习适用于个人级数据和总结统计.
主要成果:
- 鉴定出低骨矿物质密度是secukinumab在结性脊柱炎疗效的预测因素.
- 发现特定的吸烟者亚群受到支气管扩展剂的负面影响.
- 证明了在推断HTE时遗传变异的有用性.
结论:
- M-Learner框架有效地识别了基因驱动的HTE.
- 遗传变异是了解治疗反应变异性的宝贵工具.
- 通过实现更个性化的治疗策略,推进精准医学.
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